Antioxidative Sirt1 and the Keap1-Nrf2 Signaling Pathway Impair Inflammation and Positively Regulate Autophagy in Murine Mammary Epithelial Cells or Mammary Glands Infected with Streptococcus uberis
Bibliographic record
Abstract
Streptococcus uberis mastitis in cattle infects mammary epithelial cells. Although oxidative responses often remove intracellular microbes, S. uberis survives, but the mechanisms are not well understood. Herein, we aimed to elucidate antioxidative mechanisms during pathogenesis of S. uberis after isolation from clinical bovine mastitis milk samples. S. uberis’s in vitro pathomorphology, oxidative stress biological activities, transcription of antioxidative factors, inflammatory response cytokines, autophagosome and autophagy functions were evaluated, and in vivo S. uberis was injected into the fourth mammary gland nipple of each mouse to assess the infectiousness of S. uberis potential molecular mechanisms. The results showed that infection with S. uberis induced early oxidative stress and increased reactive oxygen species (ROS). However, over time, ROS concentrations decreased due to increased antioxidative activity, including total superoxide dismutase (T-SOD) and malondialdehyde (MDA) enzymes, plus transcription of antioxidative factors (Sirt1, Keap1, Nrf2, HO-1). Treatment with a ROS scavenger (N-acetyl cysteine, NAC) before infection with S. uberis reduced antioxidative responses and the inflammatory response, including the cytokines IL-6 and TNF-α, and the formation of the Atg5-LC3II/LC3I autophagosome. Synthesis of antioxidants determined autophagy functions, with Sirt1/Nrf2 activating autophagy in the presence of S. uberis. This study demonstrated the evasive mechanisms of S. uberis in mastitis, including suppressing inflammatory and ROS defenses by stimulating antioxidative pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".